Businesses in industries such as financial services, insurance, travel and hospitality, retail, cable and satellite television rely on voice contact with customers to answer client inquiries, make sales, and provide technical support. The increasing sophistication and use of smart phones and advanced mobile devices by customers has led to a more direct relationship in how advertisements drive phone calls. In particular, because customers are increasingly receiving advertisements on the same device used to make phone calls, the resulting ease of responding to advertisements by phone has facilitated closer relationships between customers and advertisers. For businesses across multiple domains or industries who send advertisements, every contact with a customer is an opportunity to make a lasting impression, to gather customer data, or to strengthen a customer's loyalty to the business. With regard to customer calls, it is desirable to know whether customers are receiving quality customer service that includes accurate information, adherence to professional communication standards, and the conveyance of a feeling of being valued by the business. It is also desirable for the business to understand what occurs during a phone call and how specific advertising campaigns affect the outcome of calls. This understanding can allow companies to better understand information relating to marketing campaigns, including how lead generation results in customer sales and retention.
One method used by businesses to track and analyze voice transactions is call recordation, with or without transcription. By listening to recorded customer calls (in their entirety or in samples), or by reviewing the transcripts of recorded customer calls, businesses hope to gain insight from conversations with real customers. However, the recording and transcription of calls incurs several problems and disadvantages, such as agent and/or caller objections and the need for expensive and specialized equipment. Recording and transcription of calls also raise legal and privacy concerns. For example, recording or transcribing information regarding an individual's medical health may violate certain laws, such as the Health Insurance Portability and Accountability Act (HIPAA). Similarly, federal and state privacy laws, as well as a general privacy concerns of the public, may arise when recording or transcribing certain personal information such as Social Security Numbers, credit card numbers, bank account numbers, passport numbers, physical street addresses, Protected Health Information (PHI), or other Personally Identifiable Information (PII). As a further example, recording or transcribing information may raise concerns regarding certain state and federal anti-discrimination laws with regard to national origin, race, color, religion, age, gender, pregnancy, sexual orientation, citizenship, familial status, disability status, veteran status, genetic information, or any additional number of protected classes under state or federal law. Moreover, as the volume of calls increases, the amount of storage and processing associated with maintaining a record of every call becomes increasingly prohibitive.
Another technique which businesses utilize to evaluate calls and boost advertising performance is known as call mining or keyword spotting. In call mining, businesses identify key words and phrases to be tracked in every call (e.g., “credit card,” “appointment,” “thank you,” “sale”) so as to determine which calls were converted into sales or appointments, and caller intent, needs and pain points. While the aim of call mining is to find successful outcomes and conversions, call mining is based on the words spoken in the call, acquired in either a manual or automated fashion. In general the calls must be recorded or transcribed, which as noted can be costly or prohibited for various reasons. In some cases, a transcript can be inaccurate because the source audio was of poor quality. Those skilled in the art will appreciated that the source audio may be real time, near real-time, transcribed, or a combination thereof. In other cases, a transcript can be inaccurate because large vocabulary speech recognition generally becomes less accurate as the size of the vocabulary grows larger. Therefore, if a relatively large vocabulary (e.g., the entire English dictionary) is needed, the resulting speech recognition may tend to be inaccurate. Moreover, at times, the vocabulary of the conversation can be foreign to a transcriber, and the transcribed results of low accuracy. Other schemes to analyze call outcomes such as live monitoring can also involve additional costs and drawbacks. As such, previous attempts at call mining have often proven to be inaccurate, unworkable, or prohibitively expensive.